{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "【课程2.1】  缺失值处理\n",
    "\n",
    "数据缺失主要包括记录缺失和字段信息缺失等情况，其对数据分析会有较大影响，导致结果不确定性更加显著\n",
    "\n",
    "缺失值的处理：删除记录 / 数据插补 / 不处理\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "4     True\n",
      "5     True\n",
      "6    False\n",
      "7    False\n",
      "8     True\n",
      "9    False\n",
      "dtype: bool\n",
      "   value1 value2\n",
      "0    True   True\n",
      "1    True   True\n",
      "2    True   True\n",
      "3    True   True\n",
      "4   False   True\n",
      "5   False  False\n",
      "6    True  False\n",
      "7    True   True\n",
      "8   False   True\n",
      "9    True  False\n",
      "10   True   True\n",
      "0      True\n",
      "1      True\n",
      "2      True\n",
      "3      True\n",
      "4     False\n",
      "5     False\n",
      "6      True\n",
      "7      True\n",
      "8     False\n",
      "9      True\n",
      "10     True\n",
      "Name: value1, dtype: bool\n",
      "------\n",
      "0    12.0\n",
      "1    33.0\n",
      "2    45.0\n",
      "3    23.0\n",
      "6    66.0\n",
      "7    54.0\n",
      "9    99.0\n",
      "dtype: float64\n",
      "0      12.0\n",
      "1      33.0\n",
      "2      45.0\n",
      "3      23.0\n",
      "4       NaN\n",
      "7      54.0\n",
      "8       NaN\n",
      "10    190.0\n",
      "Name: value1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 判断是否有缺失值数据 - isnull，notnull\n",
    "# isnull：缺失值为True，非缺失值为False\n",
    "# notnull：缺失值为False，非缺失值为True\n",
    "\n",
    "s = pd.Series([12,33,45,23,np.nan,np.nan,66,54,np.nan,99])\n",
    "df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190],\n",
    "                  'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']})\n",
    "# 创建数据\n",
    "\n",
    "print(s.isnull())  # Series直接判断是否是缺失值，返回一个Series\n",
    "print(df.notnull())  # Dataframe直接判断是否是缺失值，返回一个Series\n",
    "print(df['value1'].notnull())  # 通过索引判断\n",
    "print('------')\n",
    "\n",
    "s2 = s[s.isnull() == False]  \n",
    "df2 = df[df['value2'].notnull()]    # 注意和 df2 = df[df['value2'].notnull()] ['value1'] 的区别\n",
    "print(s2)\n",
    "print(df2)\n",
    "# 筛选非缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    12.0\n",
      "1    33.0\n",
      "2    45.0\n",
      "3    23.0\n",
      "6    66.0\n",
      "7    54.0\n",
      "9    99.0\n",
      "dtype: float64\n",
      "0      12.0\n",
      "1      33.0\n",
      "2      45.0\n",
      "3      23.0\n",
      "6      66.0\n",
      "7      54.0\n",
      "9      99.0\n",
      "10    190.0\n",
      "Name: value1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 删除缺失值 - dropna\n",
    "\n",
    "s = pd.Series([12,33,45,23,np.nan,np.nan,66,54,np.nan,99])\n",
    "df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190],\n",
    "                  'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']})\n",
    "# 创建数据\n",
    "\n",
    "s.dropna(inplace = True)\n",
    "df2 = df['value1'].dropna()\n",
    "print(s)\n",
    "print(df2)\n",
    "# drop方法：可直接用于Series，Dataframe\n",
    "# 注意inplace参数，默认False → 生成新的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    12.0\n",
      "1    33.0\n",
      "2    45.0\n",
      "3    23.0\n",
      "4     0.0\n",
      "5     0.0\n",
      "6    66.0\n",
      "7    54.0\n",
      "8     0.0\n",
      "9    99.0\n",
      "dtype: float64\n",
      "------\n",
      "    value1 value2\n",
      "0     12.0      a\n",
      "1     33.0      b\n",
      "2     45.0      c\n",
      "3     23.0      d\n",
      "4     23.0      e\n",
      "5     23.0    NaN\n",
      "6     66.0    NaN\n",
      "7     54.0      f\n",
      "8     54.0      g\n",
      "9     99.0    NaN\n",
      "10   190.0      g\n",
      "------\n",
      "0        1\n",
      "1        1\n",
      "2        1\n",
      "3        1\n",
      "4        2\n",
      "5        2\n",
      "6        2\n",
      "7        3\n",
      "8        4\n",
      "9        5\n",
      "10    缺失数据\n",
      "11    缺失数据\n",
      "12      66\n",
      "13      54\n",
      "14    缺失数据\n",
      "15      99\n",
      "dtype: object\n",
      "------\n",
      "0      NaN\n",
      "1      NaN\n",
      "2      NaN\n",
      "3      NaN\n",
      "4      NaN\n",
      "5      NaN\n",
      "6      NaN\n",
      "7      NaN\n",
      "8        4\n",
      "9        5\n",
      "10    缺失数据\n",
      "11    缺失数据\n",
      "12      66\n",
      "13      54\n",
      "14    缺失数据\n",
      "15      99\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 填充/替换缺失数据 - fillna、replace\n",
    "\n",
    "s = pd.Series([12,33,45,23,np.nan,np.nan,66,54,np.nan,99])\n",
    "df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190],\n",
    "                  'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']})\n",
    "# 创建数据\n",
    "\n",
    "s.fillna(0,inplace = True)\n",
    "print(s)\n",
    "print('------')\n",
    "# s.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)\n",
    "# value：填充值\n",
    "# 注意inplace参数\n",
    "\n",
    "df['value1'].fillna(method = 'pad',inplace = True)\n",
    "print(df)\n",
    "print('------')\n",
    "# method参数：\n",
    "# pad / ffill → 用之前的数据填充 \n",
    "# backfill / bfill → 用之后的数据填充 \n",
    "\n",
    "s = pd.Series([1,1,1,1,2,2,2,3,4,5,np.nan,np.nan,66,54,np.nan,99])\n",
    "s.replace(np.nan,'缺失数据',inplace = True)\n",
    "print(s)\n",
    "print('------')\n",
    "# df.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)\n",
    "# to_replace → 被替换的值\n",
    "# value → 替换值\n",
    "\n",
    "s.replace([1,2,3],np.nan,inplace = True)\n",
    "print(s)\n",
    "# 多值用np.nan代替"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------\n",
      "均值为：4.56, 中位数为：4.50\n",
      "众数为： [5.0]\n",
      "------\n",
      "0      1.0000\n",
      "1      2.0000\n",
      "2      3.0000\n",
      "3      4.5625\n",
      "4      3.0000\n",
      "5      4.0000\n",
      "6      5.0000\n",
      "7      5.0000\n",
      "8      5.0000\n",
      "9      5.0000\n",
      "10     4.5625\n",
      "11     4.5625\n",
      "12     6.0000\n",
      "13     6.0000\n",
      "14     7.0000\n",
      "15    12.0000\n",
      "16     2.0000\n",
      "17     4.5625\n",
      "18     3.0000\n",
      "19     4.0000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 缺失值插补\n",
    "# 几种思路：均值/中位数/众数插补、临近值插补、插值法\n",
    "# （1）均值/中位数/众数插补\n",
    "\n",
    "s = pd.Series([1,2,3,np.nan,3,4,5,5,5,5,np.nan,np.nan,6,6,7,12,2,np.nan,3,4])\n",
    "#print(s)\n",
    "print('------')\n",
    "# 创建数据\n",
    "\n",
    "u = s.mean()     # 均值\n",
    "me = s.median()  # 中位数\n",
    "mod = s.mode()   # 众数\n",
    "print('均值为：%.2f, 中位数为：%.2f' % (u,me))\n",
    "print('众数为：', mod.tolist())\n",
    "print('------')\n",
    "# 分别求出均值/中位数/众数\n",
    "\n",
    "s.fillna(u,inplace = True)\n",
    "print(s)\n",
    "# 用均值填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------\n",
      "0      1.0\n",
      "1      2.0\n",
      "2      3.0\n",
      "3      3.0\n",
      "4      3.0\n",
      "5      4.0\n",
      "6      5.0\n",
      "7      5.0\n",
      "8      5.0\n",
      "9      5.0\n",
      "10     5.0\n",
      "11     5.0\n",
      "12     6.0\n",
      "13     6.0\n",
      "14     7.0\n",
      "15    12.0\n",
      "16     2.0\n",
      "17     2.0\n",
      "18     3.0\n",
      "19     4.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 缺失值插补\n",
    "# 几种思路：均值/中位数/众数插补、临近值插补、插值法\n",
    "# （2）临近值插补\n",
    "\n",
    "s = pd.Series([1,2,3,np.nan,3,4,5,5,5,5,np.nan,np.nan,6,6,7,12,2,np.nan,3,4])\n",
    "#print(s)\n",
    "print('------')\n",
    "# 创建数据\n",
    "\n",
    "s.fillna(method = 'ffill',inplace = True)\n",
    "print(s)\n",
    "# 用前值插补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         2\n",
      "-0.1111 x + 0.3333 x + 10\n",
      "<class 'numpy.lib.polynomial.poly1d'>\n",
      "插值10为：2.22\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "# 缺失值插补\n",
    "# 几种思路：均值/中位数/众数插补、临近值插补、插值法\n",
    "# （3）插值法 —— 拉格朗日插值法\n",
    "\n",
    "from scipy.interpolate import lagrange\n",
    "x = [3, 6, 9]\n",
    "y = [10, 8, 4]\n",
    "print(lagrange(x,y))\n",
    "print(type(lagrange(x,y)))\n",
    "# 的输出值为的是多项式的n个系数\n",
    "# 这里输出3个值，分别为a0,a1,a2\n",
    "# y = a0 * x**2 + a1 * x + a2 → y = -0.11111111 * x**2 + 0.33333333 * x + 10\n",
    "\n",
    "print('插值10为：%.2f' % lagrange(x,y)(10))\n",
    "print('------')\n",
    "# -0.11111111*100 + 0.33333333*10 + 10 = -11.11111111 + 3.33333333 +10 = 2.22222222"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    17.824704\n",
      "1    17.585902\n",
      "2    31.765869\n",
      "3          NaN\n",
      "4    51.779036\n",
      "dtype: float64\n",
      "总数据量:100\n",
      "------\n",
      "缺失值数据量:9\n",
      "缺失数据占比:9.00%\n",
      "55.4089720353\n",
      "71.8125483135\n",
      "64.3771972656\n",
      "114.778320313\n",
      "96.9274902344\n",
      "-0.108276367188\n",
      "-79.5625\n",
      "836.0\n",
      "1208.0\n",
      "finished!\n"
     ]
    },
    {
     "data": {
      "image/png": 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2s7bRm1/XEJIZZYTBeCdHD5GGmNlphD6MOxMGjr81tt3RBTLZ1Zr4WehT+DHC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\nqxLyFNNvuAEKCuDhh4PVFFJRYWFwurI6yqifT5GQS8R4vfz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edzUwBfgf4GygMTDbdQwi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\nv7jMM67m5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4gR6Wm5Zs/OuhKp5DzzTHrwYs+eWVciSSonjz/+OG3atGHUqFFZlyKVrRde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\n8G+Fe1uSmmkU8PCmH5Ik2Qg8Tb1veekfB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      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb05fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 缺失值插补\n",
    "# 几种思路：均值/中位数/众数插补、临近值插补、插值法\n",
    "# （3）插值法 —— 拉格朗日插值法，实际运用\n",
    "\n",
    "data = pd.Series(np.random.rand(100)*100)\n",
    "data[3,6,33,56,45,66,67,80,90] = np.nan\n",
    "print(data.head())\n",
    "print('总数据量:%i' % len(data))\n",
    "print('------')\n",
    "# 创建数据\n",
    "\n",
    "data_na = data[data.isnull()]\n",
    "print('缺失值数据量:%i' % len(data_na))\n",
    "print('缺失数据占比:%.2f%%' % (len(data_na) / len(data) * 100))\n",
    "# 缺失值的数量\n",
    "\n",
    "data_c = data.fillna(data.median())  #  中位数填充缺失值\n",
    "fig,axes = plt.subplots(1,4,figsize = (20,5))\n",
    "data.plot.box(ax = axes[0],grid = True,title = '数据分布')\n",
    "data.plot(kind = 'kde',style = '--r',ax = axes[1],grid = True,title = '删除缺失值',xlim = [-50,150])\n",
    "data_c.plot(kind = 'kde',style = '--b',ax = axes[2],grid = True,title = '缺失值填充中位数',xlim = [-50,150])\n",
    "# 密度图查看缺失值情况\n",
    "\n",
    "def na_c(s,n,k=5):\n",
    "    y = s[list(range(n-k,n+1+k))] # 取数\n",
    "    y = y[y.notnull()]  # 剔除空值\n",
    "    return(lagrange(y.index,list(y))(n))\n",
    "# 创建函数，做插值，由于数据量原因，以空值前后5个数据（共10个数据）为例做插值\n",
    "\n",
    "na_re = []\n",
    "for i in range(len(data)):\n",
    "    if data.isnull()[i]:\n",
    "        data[i] = na_c(data,i)\n",
    "        print(na_c(data,i))\n",
    "        na_re.append(data[i])\n",
    "data.dropna(inplace=True)  # 清除插值后仍存在的缺失值\n",
    "data.plot(kind = 'kde',style = '--k',ax = axes[3],grid = True,title = '拉格朗日插值后',xlim = [-50,150])\n",
    "print('finished!')\n",
    "# 缺失值插值"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
